code: def simulate_single_motif_detection(
      motif_name, seq_length, num_pos, num_neg, GC_fraction
  ):
      """Simulates two classes of seqeuences:
      - Positive class sequence with a motif
          embedded anywhere in the sequence
      - Negative class sequence without the motif
  
      Parameters
      ----------
      motif_name : str
          encode motif name
      seq_length : int
          length of sequence
      num_pos : int
          number of positive class sequences
      num_neg : int
          number of negative class sequences
      GC_fraction : float
          GC fraction in background sequence
  
      Returns
      -------
      sequence_arr : 1darray
          Array with sequence strings.
      y : 1darray
          Array with positive/negative class labels.
      embedding_arr: 1darray
          Array of embedding objects.
      """
      (
          motif_sequence_arr,
          positive_embedding_arr,
      ) = simple_motif_embedding(
          motif_name, seq_length, num_pos, GC_fraction
      )
      (
          random_sequence_arr,
          negative_embedding_arr,
      ) = simple_motif_embedding(None, seq_length, num_neg, GC_fraction)
      sequence_arr = np.concatenate(
          (motif_sequence_arr, random_sequence_arr)
      )
      y = np.array([[True]] * num_pos + [[False]] * num_neg)
      embedding_arr = positive_embedding_arr + negative_embedding_arr
      return sequence_arr, y, embedding_arr
  
file_path: deepchem/deepchem/molnet/dnasim.py
imports: import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: simulate_single_motif_detection(motif_name, seq_length, num_pos, num_neg, GC_fraction) and returns: sequence_arr,  y,  embedding_arr